solon-ai

To create a Solon-AI application that integrates with SpringBoot to implement a Retrieval-Augmented Generation (RAG) feature, we'll follow these steps:

1. **Project Setup**: Initialize a new SpringBoot project with Solon-AI dependencies. The key dependencies include `solon-ai-core`, `solon-ai-rag`, and `spring-boot-starter-web`. The project structure will look like this:

```
com.example.ragapp
├── controllers
│   └── RAGController.java
├── services
│   └── RAGService.java
├── models
│   └── RAGRequest.java
├── config
│   └── RAGConfig.java
└── Application.java
```

2. **Configuration**: In `RAGConfig.java`, configure the RAG service with the necessary parameters such as the vector database connection and the LLM endpoint. Here's a sample configuration:

```java
@Configuration
public class RAGConfig {
    @Bean
    public RAGService ragService() {
        return new RAGService("vector_db_connection", "llm_endpoint");
    }
}
```

3. **Service Implementation**: Implement the `RAGService` class to handle the RAG logic. This includes querying the vector database, retrieving relevant documents, and generating responses using the LLM. Here's a sample implementation:

```java
public class RAGService {
    private final String vectorDbConnection;
    private final String llmEndpoint;

    public RAGService(String vectorDbConnection, String llmEndpoint) {
        this.vectorDbConnection = vectorDbConnection;
        this.llmEndpoint = llmEndpoint;
    }

    public String generateResponse(String query) {
        // Query the vector database
        List<String> relevantDocuments = queryVectorDatabase(query);
        // Generate response using LLM
        return generateResponseWithLLM(query, relevantDocuments);
    }

    private List<String> queryVectorDatabase(String query) {
        // Implementation to query the vector database
        return new ArrayList<>();
    }

    private String generateResponseWithLLM(String query, List<String> relevantDocuments) {
        // Implementation to generate response using LLM
        return "Generated response";
    }
}
```

4. **Controller Implementation**: Create a controller to expose the RAG functionality via REST endpoints. Here's a sample controller:

```java
@RestController
@RequestMapping("/api/rag")
public class RAGController {
    private final RAGService ragService;

    public RAGController(RAGService ragService) {
        this.ragService = ragService;
    }

    @PostMapping("/query")
    public ResponseEntity<String> query(@RequestBody RAGRequest request) {
        String response = ragService.generateResponse(request.getQuery());
        return ResponseEntity.ok(response);
    }
}
```

5. **Request and Response**: Here's a sample request and response for the `/api/rag/query` endpoint:

**Request**
```json
{
    "query": "What is the capital of France?"
}
```

**Response**
```json
"The capital of France is Paris."
```

This implementation provides a basic structure for integrating Solon-AI with SpringBoot to implement a RAG feature. You can extend this structure to include more endpoints and functionality as needed.